Journal cover Journal topic
Biogeosciences An interactive open-access journal of the European Geosciences Union
Journal topic
Discussion papers
https://doi.org/10.5194/bg-2019-252
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-2019-252
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 09 Jul 2019

Research article | 09 Jul 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Biogeosciences (BG).

Spatio-Temporal Variations and Uncertainty in Land Surface Modelling for High Latitudes: Univariate Response Analysis

Didier G. Leibovici1, Shaun Quegan1, Edward Comyn-Platt2, Gary Hayman2, Maria Val Martin3, Mathieu Guimberteau4, Arsène Druel4, Dan Zhu4, and Philippe Ciais4 Didier G. Leibovici et al.
  • 1School of Mathematics and Statistics, University of Sheffield, UK
  • 2Centre for Ecology and Hydrology, Wallingford, UK
  • 3Leverhulme Centre for Climate Change Mitigation, Animal and Plant Sciences Department, University of Sheffield, UK
  • 4Laboratoire des Sciences du Climat et de l'Environnement, Institut Pierre Simon Laplace, France

Abstract. A range of applications analysing the impact of environmental changes due to climate change, e.g. geographical spread of climate sensitive infections (CSIs), agriculture crop modelling, etc., make use of Land Surface Modelling (LSM) to predict future land surface conditions. There are multiple LSMs to choose from that account for land processes in different ways and, depending on the application, the choice of LSM and its sensitivity will have different impacts. For useful predictions for a specific application, one must therefore understand the inherent uncertainties in the LSMs and the variations between them, as well as uncertainties arising from variation in the climate data driving the LSMs. This requires methods to analyse multivariate spatio-temporal variations and differences. A methodology is proposed based on multi-way data analysis, which extends Singular Value Decomposition (SVD) to multi-dimensional tables, and provides spatio-temporal descriptions of agreements and disagreements between LSMs for both historical simulations and future predictions. The application underlying this paper is prediction of how climate change will affect the spread of CSIs in the Fenno-Scandinavian and north-west Russian regions, and the approach is explored by comparing Net Primary Production (NPP) estimates over the period 1998–2013 from versions of leading LSMs (JULES, CLM5 and two versions of ORCHIDEE) that are adapted to high latitude processes, as well as variations in JULES up to 2100 when driven by 34 global circulation models (GCMs). A single optimal spatio-temporal pattern, with slightly different weights for the four LSMs (up to 14 % maximum difference), provides a good approximation to all their estimates of NPP, capturing between 87 % and 93 % of the variability in the individual models, as well as around 90 % of the variability in the combined LSM dataset. The next best adjustment to this pattern, capturing an extra 4 % of the overall variability, is essentially a spatial correction applied to ORCHIDEE-HLveg that significantly improves the fit to this LSM, with only small improvements for the other LSMs. Subsequent correction terms gradually improve the overall and individual LSM fits, but capture at most 1.7 % of the overall variability. Analysis of differences between LSMs provides information on the times and places where the LSMs differ and by how much, but in this case no single spatio-temporal pattern strongly dominates the variability. Hence interpretation of the analysis requires the summation of several such patterns. Nonetheless, the three best principal tensors capture around 76 % of the variability in the LSM differences, and to a first approximation successively indicate the times and places where ORCHIDEE-HLveg, CLM5 and ORCHIDEE-MICT respectively differ from the other LSMs. Differences between the climate forcing GCMs had a marginal effect up to 6 % on NPP predictions out to 2100 without specific spatio-temporal GCM interaction.

Didier G. Leibovici et al.
Interactive discussion
Status: open (until 21 Aug 2019)
Status: open (until 21 Aug 2019)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement
Didier G. Leibovici et al.
Didier G. Leibovici et al.
Viewed  
Total article views: 114 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
93 19 2 114 0 2
  • HTML: 93
  • PDF: 19
  • XML: 2
  • Total: 114
  • BibTeX: 0
  • EndNote: 2
Views and downloads (calculated since 09 Jul 2019)
Cumulative views and downloads (calculated since 09 Jul 2019)
Viewed (geographical distribution)  
Total article views: 105 (including HTML, PDF, and XML) Thereof 105 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Cited  
Saved  
No saved metrics found.
Discussed  
No discussed metrics found.
Latest update: 22 Jul 2019
Publications Copernicus
Download
Short summary
Analysing the impact of environmental changes due to climate change, e.g. geographical spread of climate sensitive infections (CSIs), agriculture crop modelling, may use Land Surface Modelling (LSM) to predict future land surface conditions. There are multiple LSMs to choose from. The paper proposes a multivariate spatio-temporal data science method to understand the inherent uncertainties in 4 LSMs and the variations between them in Nordic areas for the natural primary production (NPP).
Analysing the impact of environmental changes due to climate change, e.g. geographical spread of...
Citation